{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "203a69fc-5696-4a4c-8a02-b4d67e75f182",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.path import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac112e49-a477-4564-b622-8ab7637c6933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取Excel文件\n",
    "df_member = pd.read_excel('../data/Q2.xlsx')\n",
    "df_task = pd.read_excel('../data/combined_tasks.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bd886b5-cfb0-4f81-a1d7-8d29bc6bdb7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>任务号码</th>\n",
       "      <th>任务gps纬度</th>\n",
       "      <th>任务gps经度</th>\n",
       "      <th>任务标价</th>\n",
       "      <th>任务执行情况</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0001</td>\n",
       "      <td>22.566142</td>\n",
       "      <td>113.980837</td>\n",
       "      <td>66.605203</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0002</td>\n",
       "      <td>22.686205</td>\n",
       "      <td>113.940525</td>\n",
       "      <td>67.486857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0003</td>\n",
       "      <td>22.576512</td>\n",
       "      <td>113.957198</td>\n",
       "      <td>66.081525</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0004</td>\n",
       "      <td>22.564841</td>\n",
       "      <td>114.244571</td>\n",
       "      <td>65.580365</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0005</td>\n",
       "      <td>22.558888</td>\n",
       "      <td>113.950723</td>\n",
       "      <td>65.889314</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>A0807</td>\n",
       "      <td>23.039098</td>\n",
       "      <td>113.773178</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>A0808</td>\n",
       "      <td>22.846704</td>\n",
       "      <td>114.159286</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>A0828</td>\n",
       "      <td>23.012808</td>\n",
       "      <td>113.760312</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>A0833</td>\n",
       "      <td>22.814676</td>\n",
       "      <td>113.827731</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>A0834</td>\n",
       "      <td>23.063674</td>\n",
       "      <td>113.771188</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      任务号码    任务gps纬度     任务gps经度       任务标价  任务执行情况\n",
       "0    A0001  22.566142  113.980837  66.605203       1\n",
       "1    A0002  22.686205  113.940525  67.486857       0\n",
       "2    A0003  22.576512  113.957198  66.081525       0\n",
       "3    A0004  22.564841  114.244571  65.580365       0\n",
       "4    A0005  22.558888  113.950723  65.889314       0\n",
       "..     ...        ...         ...        ...     ...\n",
       "830  A0807  23.039098  113.773178  65.500000       1\n",
       "831  A0808  22.846704  114.159286  85.000000       1\n",
       "832  A0828  23.012808  113.760312  66.000000       1\n",
       "833  A0833  22.814676  113.827731  85.000000       1\n",
       "834  A0834  23.063674  113.771188  65.500000       1\n",
       "\n",
       "[835 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6329536-2808-4c69-851e-11f4acc01288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final_cluster\n",
      "0      4\n",
      "34     4\n",
      "391    4\n",
      "209    4\n",
      "208    4\n",
      "      ..\n",
      "230    1\n",
      "229    1\n",
      "226    1\n",
      "225    1\n",
      "492    1\n",
      "Name: count, Length: 493, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np\n",
    "\n",
    "# 准备地理坐标数据\n",
    "coords = df_task[['任务gps纬度', '任务gps经度']].values\n",
    "\n",
    "# 定义1公里内的任务点为一个簇\n",
    "kms_per_radian = 6371.0088  # 地球半径，单位为公里\n",
    "epsilon = 1 / kms_per_radian  # 转换为弧度\n",
    "\n",
    "# 初次聚类\n",
    "db = DBSCAN(eps=epsilon, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))\n",
    "df_task['initial_cluster'] = db.labels_\n",
    "\n",
    "# 初始化 final_cluster 列\n",
    "df_task['final_cluster'] = -1\n",
    "\n",
    "# 初始化聚类标签\n",
    "final_cluster_id = 0\n",
    "\n",
    "# 处理初次聚类中点数超过4个的类\n",
    "for cluster_id in np.unique(df_task['initial_cluster']):\n",
    "    sub_cluster = df_task[df_task['initial_cluster'] == cluster_id]\n",
    "    \n",
    "    if len(sub_cluster) > 4:\n",
    "        sub_coords = sub_cluster[['任务gps纬度', '任务gps经度']].values\n",
    "        \n",
    "        # 将超出4个点的聚类再次进行划分\n",
    "        num_subclusters = int(np.ceil(len(sub_cluster) / 4))\n",
    "        for i in range(num_subclusters):\n",
    "            start_idx = i * 4\n",
    "            end_idx = min(start_idx + 4, len(sub_cluster))\n",
    "            df_task.loc[sub_cluster.index[start_idx:end_idx], 'final_cluster'] = final_cluster_id\n",
    "            final_cluster_id += 1\n",
    "    else:\n",
    "        df_task.loc[sub_cluster.index, 'final_cluster'] = final_cluster_id\n",
    "        final_cluster_id += 1\n",
    "\n",
    "# 检查每个聚类的大小\n",
    "cluster_sizes = df_task['final_cluster'].value_counts()\n",
    "print(cluster_sizes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01592d98-f541-4061-a3a2-22b5921d635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 假设 df_task 已经有了 'cluster' 列，表示每个任务点的聚类标签\n",
    "# 你可以将数据加载到 df_task 中\n",
    "# df_task = pd.read_csv('your_data.csv')\n",
    "\n",
    "# 统计每个聚类的数量\n",
    "cluster_counts = df_task['final_cluster'].value_counts()\n",
    "\n",
    "# 将每个任务点的颜色分配为红色或蓝色\n",
    "colors = df_task['final_cluster'].apply(lambda x: 'red' if cluster_counts[x] > 1 else 'blue')\n",
    "\n",
    "# 画散点图\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(df_task['任务gps经度'], df_task['任务gps纬度'], c=colors, s=5)  # 调整点的大小为50\n",
    "\n",
    "# 添加图例\n",
    "plt.scatter([], [], c='red', label='Clusters (>=2 points)')\n",
    "plt.scatter([], [], c='blue', label='Single points')\n",
    "\n",
    "plt.title('Task Points Clustering')\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.legend(loc='best')\n",
    "# plt.show()\n",
    "# 保存更高分辨率的图片\n",
    "plt.savefig('cluster.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64cddd23-cfe3-43e4-9186-51dcb3757d4e",
   "metadata": {},
   "source": [
    "## 预测完成率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df0519df-7097-43e4-aa47-08da2915cd57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>任务号码</th>\n",
       "      <th>任务gps纬度</th>\n",
       "      <th>任务gps经度</th>\n",
       "      <th>任务标价</th>\n",
       "      <th>任务执行情况</th>\n",
       "      <th>initial_cluster</th>\n",
       "      <th>final_cluster</th>\n",
       "      <th>预测完成率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0001</td>\n",
       "      <td>22.566142</td>\n",
       "      <td>113.980837</td>\n",
       "      <td>66.605203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0002</td>\n",
       "      <td>22.686205</td>\n",
       "      <td>113.940525</td>\n",
       "      <td>67.486857</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0003</td>\n",
       "      <td>22.576512</td>\n",
       "      <td>113.957198</td>\n",
       "      <td>66.081525</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0004</td>\n",
       "      <td>22.564841</td>\n",
       "      <td>114.244571</td>\n",
       "      <td>65.580365</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0005</td>\n",
       "      <td>22.558888</td>\n",
       "      <td>113.950723</td>\n",
       "      <td>65.889314</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>A0807</td>\n",
       "      <td>23.039098</td>\n",
       "      <td>113.773178</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>460</td>\n",
       "      <td>491</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>A0808</td>\n",
       "      <td>22.846704</td>\n",
       "      <td>114.159286</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>356</td>\n",
       "      <td>385</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>A0828</td>\n",
       "      <td>23.012808</td>\n",
       "      <td>113.760312</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>378</td>\n",
       "      <td>408</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>A0833</td>\n",
       "      <td>22.814676</td>\n",
       "      <td>113.827731</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>461</td>\n",
       "      <td>492</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>A0834</td>\n",
       "      <td>23.063674</td>\n",
       "      <td>113.771188</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>370</td>\n",
       "      <td>400</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      任务号码    任务gps纬度     任务gps经度       任务标价  任务执行情况  initial_cluster  \\\n",
       "0    A0001  22.566142  113.980837  66.605203       1                0   \n",
       "1    A0002  22.686205  113.940525  67.486857       0                1   \n",
       "2    A0003  22.576512  113.957198  66.081525       0                2   \n",
       "3    A0004  22.564841  114.244571  65.580365       0                3   \n",
       "4    A0005  22.558888  113.950723  65.889314       0                4   \n",
       "..     ...        ...         ...        ...     ...              ...   \n",
       "830  A0807  23.039098  113.773178  65.500000       1              460   \n",
       "831  A0808  22.846704  114.159286  85.000000       1              356   \n",
       "832  A0828  23.012808  113.760312  66.000000       1              378   \n",
       "833  A0833  22.814676  113.827731  85.000000       1              461   \n",
       "834  A0834  23.063674  113.771188  65.500000       1              370   \n",
       "\n",
       "     final_cluster  预测完成率  \n",
       "0                0   0.25  \n",
       "1                1   0.00  \n",
       "2                2   0.00  \n",
       "3                3   0.00  \n",
       "4                4   0.00  \n",
       "..             ...    ...  \n",
       "830            491   1.00  \n",
       "831            385   1.00  \n",
       "832            408   1.00  \n",
       "833            492   1.00  \n",
       "834            400   1.00  \n",
       "\n",
       "[835 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以基于每个聚类中任务点的历史完成率来预测整体完成率\n",
    "# 简单示例：聚类中的所有任务点都完成则聚类任务完成，否则聚类任务未完成\n",
    "\n",
    "def predict_completion_rate(cluster_id):\n",
    "    tasks_in_cluster = df_task[df_task['final_cluster'] == cluster_id]\n",
    "    completed_tasks = tasks_in_cluster['任务执行情况'].sum()\n",
    "    total_tasks = len(tasks_in_cluster)\n",
    "    completion_rate = completed_tasks / total_tasks\n",
    "    return completion_rate\n",
    "\n",
    "# 计算每个聚类的任务完成率\n",
    "df_task['预测完成率'] = df_task['final_cluster'].apply(predict_completion_rate)\n",
    "\n",
    "# 查看预测完成率\n",
    "df_task\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90c949b8-5666-4e5b-9f7b-8d7aa9cd5d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task['预测完成率'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b787d811-44a3-46fd-8ac0-c9f5b5713653",
   "metadata": {},
   "source": [
    "### 二分类计算完成率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9eff069-4028-4dd3-8c1d-09d720b85dec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已完成的任务数量: 603\n"
     ]
    }
   ],
   "source": [
    "# 确保列名正确无误\n",
    "if '预测完成率' in df_task.columns:\n",
    "    # 将任务完成率进行二分类，> 0.5 视为已完成，<= 0.5 视为未完成\n",
    "    df_task['任务是否完成'] = df_task['预测完成率'].apply(lambda x: 1 if x >= 0.5\n",
    "                                               else 0)\n",
    "\n",
    "    # 统计已完成任务的数量\n",
    "    completed_tasks_count = df_task['任务是否完成'].sum()\n",
    "\n",
    "    # 查看统计结果\n",
    "    print(f\"已完成的任务数量: {completed_tasks_count}\")\n",
    "\n",
    "    # 查看处理后的 DataFrame\n",
    "    df_task"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70f9b2d-b367-4b57-90d4-2a8f87532204",
   "metadata": {},
   "source": [
    "## 调整新价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06d1fe6c-f3e8-42be-bfd9-4383090dfe4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       任务gps纬度     任务gps经度       任务标价   优化后的任务标价  final_cluster\n",
      "0    22.566142  113.980837  66.605203  76.576235              0\n",
      "1    22.686205  113.940525  67.486857  74.221110              1\n",
      "2    22.576512  113.957198  66.081525  69.374788              2\n",
      "3    22.564841  114.244571  65.580365  68.847872              3\n",
      "4    22.558888  113.950723  65.889314  69.171632              4\n",
      "..         ...         ...        ...        ...            ...\n",
      "830  23.039098  113.773178  65.500000  65.500000            491\n",
      "831  22.846704  114.159286  85.000000  89.214610            385\n",
      "832  23.012808  113.760312  66.000000  69.286189            408\n",
      "833  22.814676  113.827731  85.000000  85.000000            492\n",
      "834  23.063674  113.771188  65.500000  72.039786            400\n",
      "\n",
      "[835 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "def optimize_price(base_price, cluster_size, distance_from_center):\n",
    "    \"\"\"\n",
    "    基础价格：base_price\n",
    "    聚类规模：cluster_size\n",
    "    距离聚类中心的距离：distance_from_center\n",
    "    \"\"\"\n",
    "    cluster_adjustment = 1 + 0.05 * (cluster_size - 1)  # 聚类规模调整因子\n",
    "    distance_adjustment = 1 - 0.05 * distance_from_center  # 距离因素调整因子\n",
    "    return base_price * cluster_adjustment * distance_adjustment\n",
    "\n",
    "# 计算每个聚类的中心点\n",
    "cluster_centers = df_task.groupby('final_cluster')[['任务gps纬度', '任务gps经度']].mean()\n",
    "\n",
    "# 初始化调整后的价格列\n",
    "df_task['优化后的任务标价'] = df_task['任务标价']  # 以原始标价作为基础\n",
    "\n",
    "# 对每个任务点执行优化定价\n",
    "for cluster_id, center in cluster_centers.iterrows():\n",
    "    cluster_tasks = df_task[df_task['final_cluster'] == cluster_id]\n",
    "    cluster_size = len(cluster_tasks)\n",
    "    \n",
    "    for idx, task in cluster_tasks.iterrows():\n",
    "        # 计算任务点到聚类中心的距离\n",
    "        distance_from_center = np.sqrt((task['任务gps纬度'] - center['任务gps纬度']) ** 2 + \n",
    "                                       (task['任务gps经度'] - center['任务gps经度']) ** 2)\n",
    "        \n",
    "        # 优化价格\n",
    "        optimized_price = optimize_price(task['任务标价'], cluster_size, distance_from_center)\n",
    "        df_task.at[idx, '优化后的任务标价'] = optimized_price\n",
    "\n",
    "# 查看优化后的数据\n",
    "print(df_task[['任务gps纬度', '任务gps经度', '任务标价', '优化后的任务标价', 'final_cluster']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6dfb3ae-1f6f-454f-afa9-549a06b91591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.97773009910345"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task['优化后的任务标价'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "834a0829-7ccc-4cfc-9d2f-e89823d4c677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60101.40463275138"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task['优化后的任务标价'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f32be536-b46e-4175-a60f-07b5c3585250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>任务号码</th>\n",
       "      <th>任务gps纬度</th>\n",
       "      <th>任务gps经度</th>\n",
       "      <th>任务标价</th>\n",
       "      <th>任务执行情况</th>\n",
       "      <th>initial_cluster</th>\n",
       "      <th>final_cluster</th>\n",
       "      <th>预测完成率</th>\n",
       "      <th>任务是否完成</th>\n",
       "      <th>优化后的任务标价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0001</td>\n",
       "      <td>22.566142</td>\n",
       "      <td>113.980837</td>\n",
       "      <td>66.605203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>76.576235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0002</td>\n",
       "      <td>22.686205</td>\n",
       "      <td>113.940525</td>\n",
       "      <td>67.486857</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>74.221110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0003</td>\n",
       "      <td>22.576512</td>\n",
       "      <td>113.957198</td>\n",
       "      <td>66.081525</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>69.374788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0004</td>\n",
       "      <td>22.564841</td>\n",
       "      <td>114.244571</td>\n",
       "      <td>65.580365</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>68.847872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0005</td>\n",
       "      <td>22.558888</td>\n",
       "      <td>113.950723</td>\n",
       "      <td>65.889314</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>69.171632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>A0807</td>\n",
       "      <td>23.039098</td>\n",
       "      <td>113.773178</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>460</td>\n",
       "      <td>491</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>65.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>A0808</td>\n",
       "      <td>22.846704</td>\n",
       "      <td>114.159286</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>356</td>\n",
       "      <td>385</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>89.214610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>A0828</td>\n",
       "      <td>23.012808</td>\n",
       "      <td>113.760312</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>378</td>\n",
       "      <td>408</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>69.286189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>A0833</td>\n",
       "      <td>22.814676</td>\n",
       "      <td>113.827731</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>461</td>\n",
       "      <td>492</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>A0834</td>\n",
       "      <td>23.063674</td>\n",
       "      <td>113.771188</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>370</td>\n",
       "      <td>400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>72.039786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      任务号码    任务gps纬度     任务gps经度       任务标价  任务执行情况  initial_cluster  \\\n",
       "0    A0001  22.566142  113.980837  66.605203       1                0   \n",
       "1    A0002  22.686205  113.940525  67.486857       0                1   \n",
       "2    A0003  22.576512  113.957198  66.081525       0                2   \n",
       "3    A0004  22.564841  114.244571  65.580365       0                3   \n",
       "4    A0005  22.558888  113.950723  65.889314       0                4   \n",
       "..     ...        ...         ...        ...     ...              ...   \n",
       "830  A0807  23.039098  113.773178  65.500000       1              460   \n",
       "831  A0808  22.846704  114.159286  85.000000       1              356   \n",
       "832  A0828  23.012808  113.760312  66.000000       1              378   \n",
       "833  A0833  22.814676  113.827731  85.000000       1              461   \n",
       "834  A0834  23.063674  113.771188  65.500000       1              370   \n",
       "\n",
       "     final_cluster  预测完成率  任务是否完成   优化后的任务标价  \n",
       "0                0   0.25       0  76.576235  \n",
       "1                1   0.00       0  74.221110  \n",
       "2                2   0.00       0  69.374788  \n",
       "3                3   0.00       0  68.847872  \n",
       "4                4   0.00       0  69.171632  \n",
       "..             ...    ...     ...        ...  \n",
       "830            491   1.00       1  65.500000  \n",
       "831            385   1.00       1  89.214610  \n",
       "832            408   1.00       1  69.286189  \n",
       "833            492   1.00       1  85.000000  \n",
       "834            400   1.00       1  72.039786  \n",
       "\n",
       "[835 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task"
   ]
  },
  {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>任务号码</th>\n",
       "      <th>任务gps纬度</th>\n",
       "      <th>任务gps经度</th>\n",
       "      <th>任务标价</th>\n",
       "      <th>优化后的任务标价</th>\n",
       "      <th>final_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0001</td>\n",
       "      <td>22.566142</td>\n",
       "      <td>113.980837</td>\n",
       "      <td>66.605203</td>\n",
       "      <td>76.576235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0002</td>\n",
       "      <td>22.686205</td>\n",
       "      <td>113.940525</td>\n",
       "      <td>67.486857</td>\n",
       "      <td>74.221110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0003</td>\n",
       "      <td>22.576512</td>\n",
       "      <td>113.957198</td>\n",
       "      <td>66.081525</td>\n",
       "      <td>69.374788</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0004</td>\n",
       "      <td>22.564841</td>\n",
       "      <td>114.244571</td>\n",
       "      <td>65.580365</td>\n",
       "      <td>68.847872</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0005</td>\n",
       "      <td>22.558888</td>\n",
       "      <td>113.950723</td>\n",
       "      <td>65.889314</td>\n",
       "      <td>69.171632</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>A0807</td>\n",
       "      <td>23.039098</td>\n",
       "      <td>113.773178</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>A0808</td>\n",
       "      <td>22.846704</td>\n",
       "      <td>114.159286</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>89.214610</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>A0828</td>\n",
       "      <td>23.012808</td>\n",
       "      <td>113.760312</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>69.286189</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>A0833</td>\n",
       "      <td>22.814676</td>\n",
       "      <td>113.827731</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>A0834</td>\n",
       "      <td>23.063674</td>\n",
       "      <td>113.771188</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>72.039786</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      任务号码    任务gps纬度     任务gps经度       任务标价   优化后的任务标价  final_cluster\n",
       "0    A0001  22.566142  113.980837  66.605203  76.576235              0\n",
       "1    A0002  22.686205  113.940525  67.486857  74.221110              1\n",
       "2    A0003  22.576512  113.957198  66.081525  69.374788              2\n",
       "3    A0004  22.564841  114.244571  65.580365  68.847872              3\n",
       "4    A0005  22.558888  113.950723  65.889314  69.171632              4\n",
       "..     ...        ...         ...        ...        ...            ...\n",
       "830  A0807  23.039098  113.773178  65.500000  65.500000            491\n",
       "831  A0808  22.846704  114.159286  85.000000  89.214610            385\n",
       "832  A0828  23.012808  113.760312  66.000000  69.286189            408\n",
       "833  A0833  22.814676  113.827731  85.000000  85.000000            492\n",
       "834  A0834  23.063674  113.771188  65.500000  72.039786            400\n",
       "\n",
       "[835 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_task[['任务号码', '任务gps纬度', '任务gps经度', '任务标价', '优化后的任务标价', 'final_cluster']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e854b76-c55d-47f6-9b24-ecd35aa9fa51",
   "metadata": {},
   "outputs": [],
   "source": []
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